"""simple docstring"""


import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						return EnvironmentCommand()





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						return EnvironmentCommand(args.accelerate_config_file							)






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				@staticmethod
				def 						_a      ( _lowerCamelCase ):
										lowerCamelCase__   =parser.add_parser("env" )
										download_parser.set_defaults(func=_lowerCamelCase )
										download_parser.add_argument(
										    "--accelerate-config_file"      ,					default=_lowerCamelCase      ,					help="The accelerate config file to use for the default values in the launching script."      ,					)
										download_parser.set_defaults(func=_lowerCamelCase )

				def __init__( self      ,					_lowerCamelCase      ,					*_lowerCamelCase ):
										lowerCamelCase__   =accelerate_config_file

				def 						_a      ( self ):
										lowerCamelCase__   ="not installed"
										if is_safetensors_available():
																import safetensors

																lowerCamelCase__   =safetensors.__version__
										elif importlib.util.find_spec("safetensors" ) is not None:
																import safetensors

																lowerCamelCase__   =F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

										lowerCamelCase__   ="not installed"
										lowerCamelCase__   =lowerCamelCase__   ="not found"
										if is_accelerate_available():
																import accelerate
																from accelerate.commands.config import default_config_file, load_config_from_file

																lowerCamelCase__   =accelerate.__version__
																# Get the default from the config file.
																if self._accelerate_config_file is not None or os.path.isfile(_lowerCamelCase ):
																						lowerCamelCase__   =load_config_from_file(self._accelerate_config_file ).to_dict()

																lowerCamelCase__   =(
																    "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
																    if isinstance(_lowerCamelCase      ,					_lowerCamelCase )
																    else F'''\t{accelerate_config}'''
																)

										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="NA"
										if is_torch_available():
																import torch

																lowerCamelCase__   =torch.__version__
																lowerCamelCase__   =torch.cuda.is_available()

										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="NA"
										if is_tf_available():
																import tensorflow as tf

																lowerCamelCase__   =tf.__version__
																try:
																						# deprecated in v2.1
																						lowerCamelCase__   =tf.test.is_gpu_available()
																except AttributeError:
																						# returns list of devices, convert to bool
																						lowerCamelCase__   =bool(tf.config.list_physical_devices("GPU" ) )

										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="NA"
										if is_flax_available():
																import flax
																import jax
																import jaxlib

																lowerCamelCase__   =flax.__version__
																lowerCamelCase__   =jax.__version__
																lowerCamelCase__   =jaxlib.__version__
																lowerCamelCase__   =jax.lib.xla_bridge.get_backend().platform

										lowerCamelCase__   ={
										    "`transformers` version": version,
										    "Platform": platform.platform(),
										    "Python version": platform.python_version(),
										    "Huggingface_hub version": huggingface_hub.__version__,
										    "Safetensors version": F'''{safetensors_version}''',
										    "Accelerate version": F'''{accelerate_version}''',
										    "Accelerate config": F'''{accelerate_config_str}''',
										    "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
										    "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
										    "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
										    "Jax version": F'''{jax_version}''',
										    "JaxLib version": F'''{jaxlib_version}''',
										    "Using GPU in script?": "<fill in>",
										    "Using distributed or parallel set-up in script?": "<fill in>",
										}

										print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
										print(self.format_dict(_lowerCamelCase ) )

										return info






				@staticmethod
				def 						_a      ( _lowerCamelCase ):
										return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"



"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


